论文标题
使用稀疏表示形式增强逻辑程序的线性代数计算
Enhancing Linear Algebraic Computation of Logic Programs Using Sparse Representation
论文作者
论文摘要
近年来，逻辑程序的代数表征受到了越来越多的关注。研究人员试图利用线性代数计算与符号计算之间的联系，以便在大规模知识库中进行逻辑推断。本文通过使用稀疏矩阵将逻辑程序嵌入向量空间中提出进一步的改进。我们显示了它的巨大计算能力，可以从初始向量到达直接后果运算符的固定点。特别是，计算最小模型确定程序的性能会以这种方式显着提高。我们还将方法应用于稳定模型的普通程序模型，其中猜测与初始矩阵相关联，并在否定数量时验证其效果。这些结果显示了计算程序后果的性能方面的良好增强，并描述了张力逻辑程序的潜在力量。
Algebraic characterization of logic programs has received increasing attention in recent years. Researchers attempt to exploit connections between linear algebraic computation and symbolic computation in order to perform logical inference in large scale knowledge bases. This paper proposes further improvement by using sparse matrices to embed logic programs in vector spaces. We show its great power of computation in reaching the fixpoint of the immediate consequence operator from the initial vector. In particular, performance for computing the least models of definite programs is dramatically improved in this way. We also apply the method to the computation of stable models of normal programs, in which the guesses are associated with initial matrices, and verify its effect when there are small numbers of negation. These results show good enhancement in terms of performance for computing consequences of programs and depict the potential power of tensorized logic programs.
